{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5bab96b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5b963b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = random.binomial(10,0.5,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3358759f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5, 3, 9, 6, 6, 3, 5, 7, 5, 3, 5, 4, 3, 4, 5, 1, 7, 4, 5])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "290f0fd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "079b7ca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 100\n",
    "p = 0.5\n",
    "k = np.arange(20,81)\n",
    "binomial = stats.binom.pmf(k,n,p)\n",
    "plt.plot(k, binomial, 'o-')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "714db4f3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
